Secure systems require identification/authentication of users and protection of keys/passwords used for encryption of data. Prior art, related to the current invention falls into three categories: biometric feature extraction/recording, protecting biometric data, and combining biometrics and key embedding.
Biometrics generally are methods of identifying or verifying the identity of a person based on a physiological characteristic, with the constraint that the characteristics are relatively unique to an individual and do not change significantly over time. There is a plethora of prior art describing feature extraction, recording, and use of biometric parameters unrelated to the secure storage of such biometric parameters or key management. Examples of the features measured are: face, fingerprints, hand geometry, palm prints, iris, retina, vein, and voice. To be most effective, features to be measured should be distinctive between people and have a sufficient level of invariance over the lifetime of the person. Biometric technologies are becoming the foundation of an extensive array of highly secure identification and personal verification solutions. Because our invention is concerned with the secure mixing of biometrics and keys, and largely independent of the details of how the biometric parameters are acquired, details of this category of prior art are omitted
By definition, physical biometrics are not changeable, and must be protected because they present serious security risks if they are compromised. “Biometric authentication system with encrypted models” (U.S. Pat. No. 6,317,834), discuses the risks and teaches an approach of encrypting and decrypting biometrics. The encryption can be further enhanced, as taught by “Biometrics template” (U.S. Pat. No. 7,302,583) by splitting the key, using key-shares. Traditional encryption approaches, including '834 and '583, provide only limited protection for stored biometrics because the stored data must be decrypted for each use, so the keys are available to both parties when used. More generally the asymmetric and non-revocable nature of biometrics, even with encryption, presents other issues for both privacy and security concerns. In particular, one party, say Alice, has the store of biometric data for matching and must protect that data store. The other, say Bob, has a live sample for verification. Either Bob can send his raw “live” biometric data to Alice and trust she will protect the data and is the proper source for matching and must trust her stated results. Alternatively, Alice can send the matching data to Bob (e.g. for a match-on-card biometric to protect privacy) and then trust the result when Bob says it matches or not. Either way, one side must place considerable trust in the other, for both matching “results” and for protection of the privacy/security of the data, including encryption/decryption keys. This directly limits the trust some organizations or people will place in biometric solutions. It also limits remote authentication, e.g. web-based biometric authentication, where a “man-in-the-middle” could capture the biometric data and any keys. In summary, to safely transmit, or store, biometrics requires pre-shared keys for encryption and trusting the other party with the keys and biometric data.
An alternative approach to protecting biometric data is to transform the data into some form of revocable token, where unlike the unique characteristics of biometrics, the user can have multiple different revocable biometrically-derived identity tokens. Multiple versions of biometric-based identity tokens have been developed including “System and method for distorting a biometric for transactions with enhanced security and privacy” (U.S. Pat. No. 6,836,554 B1). This patent teaches of using non-invertible distortions to protect data. It is worth noting that the conversion of the original biometric sample into any standard biometric template is formally non-invertible, as data is lost, yet the need to protect the template motivates their work. What matters is not formal mathematical non-invertiblity of the transform, but the level of effort needed to recover an approximate representation that effectively matches the original data. The function Y=X2 is not invertible, but given Y only takes 2 guess to find X. Their general approach, based solely on non-invertible distortions, does not provide sufficient protection of the underlying biometric data to be considered secure. No detailed of actual secure transforms are presented.
Another approach in the prior art is the extraction of a small number of unique bits from the biometric data, which is then combined with cryptographic data to provide a key that depends on both the biometric and cryptographic data. Such an invention is discussed in “Biometric certificates” (U.S. Pat. No. 6,310,966) and in “Generating user-dependent keys and random numbers” (U.S. Pat. No. 6,687,375). These patents teach ways of using n bits of data obtained from a biometric to mix with a cryptographic key. The basic concept is obvious, but how to obtain n bits that are both stable and relatively unique are not and the patents do not present processes to reliably obtain that stable n-bit input. The issue of finding stable subsets of data, with the addition of error correction, is discussed in “Biometric based user authentication with syndrome codes” US Patent Application 20060123239 and Biometric Based User Authentication and Data Encryption (US Patent Application 20070174633). In these works, syndrome codes based on Wyner-Ziv or Slepian-Wolf coding are used represent biometric data, with the claim that it can then be stored securely, while still tolerating the inherent variability of biometric data. Essentially, the security of the syndrome encoding is due to the fact that it is a compressed version of the original biometric parameter. In a similar manner, “Biometric template protection and feature handling”, (US Application 2007/0180261 A1), teaches of an approach to protection using quantization and so-called helper data to produce a token that can be revoked. An important problem with the approaches of '966, '375, '239 and '261 is these classes of solutions predetermine the level of quantization of the data and hence cannot reasonably vary the False Accept Rate (FAR) or False Reject Rate (FRR) after the generation of the biometric-based identity tokens, hence they are predetermining the tradeoff between security and ease of use. Furthermore, none of these approaches discuss the actual FAR/FRR achievable by the systems, and if the systems have a higher FAR rate, then their security can be effectively compromised as an attacker can use a data store of existing biometric data to search for a existing biometric sample that will match the stored “protected” biometric-based identity token, effectively finding an approximate inverse.
The final area of related work is in protection of keys/passwords using biometrics. As secure systems often depend on keys for protection of data, providing a means to authenticate who has access to those keys is an important part of those systems. These systems mix the key and the biometric data with the goal of simultaneously protecting both. There are two important families of works in this area, generally referred to as “Fuzzy vaults” or “Fuzzy Commitment” and Biometric Encryption. Fuzzy Vaults are described in “A Fuzzy Vault Scheme,” by Juels, A., Sudan, M., in Proceedings of the 2002 IEEE International Symposium on Information Theory, June 2002; Juels and Wattenberg, “A fuzzy commitment scheme,” in Proc. of the 5th ACM Conf. on Comp. and Comm. Security, New York, N.Y., pgs. 28-36, 1999; U.S. patent application Ser. No. 09/994,476, “Order invariant fuzzy commitment system,” filed Nov. 26, 2001; with more recent work in S. Yang and I. M. Verbauwhede, “Secure fuzzy vault based fingerprint verification system,” in Asilomar Conf. on Signals, Systems, and Comp., vol. 1, pp. 577-581, November 2004. U. Uludag and A. Jain, “Fuzzy fingerprint vault,” in Proc. Workshop: Biometrics: Challenges arising from theory to practice, pp. 13-16, August 2004 and “Multibiometric Template Security Using Fuzzy Vault,” by K. Nandakumar and A. K. Jain, Proc. of the IEEE Conf on Biometrics: Theory, Applications, and Systems (BTAS 2008), The technique called Biometric encryption, is described in “Fingerprint controlled public key cryptographic system” (U.S. Pat. No. 5,541,994), “Method and apparatus for securely handling a personal identification number or cryptographic key using biometric techniques” (U.S. Pat. No. 5,712,912), and “Method for secure key management using a biometric”, (U.S. Pat. No. 6,219,794). Both Fuzzy Vaults and Biometric Encryption methods bind a key to biometric data so that the key is released only after matching with the biometrics. These approaches differ in how they bind the data to biometrics, but both families of algorithms are subject to multiple attacks to compromise the embedded keys. In “Cracking Fuzzy Vaults and Biometric Encryption”, in the Proc. 2007 IEEE Biometric Symposium, Scheirer and Boult present three attacks against these algorithms. The Attack via Record Multiplicity (ARM) shows that if an attacker can gain access to two or more instances of the “secure” tokens, these tokens can be combined to recover the underlying key and the underlying biometric data. The second attack, Serendipitous Key Inversion (SKI) shows how knowledge of the key that is released constrains the underlying biometric data—which means that whoever gets access to the released key, including the system owners, can recover most of the biometric data. Nandakumar and. Jain 2008, the authors concede that the fuzzy vault “is not a perfect template protection scheme” because of these attacks. Other, brute-force oriented, attacks against fuzzy vaults have included CRC checks (“The Fuzzy Vault for Fingerprints is Vulnerable to Brute Force Attack, P. Mihailescu. Online at http://arxiv.org/abs/0708.2974v1, 2007) and chaff point identification (“Finding the Original Point Set Hidden Among Chaff, by W. Chang, R. Shen and F. W. Teo, In Proc. of the ACM Symposium on Information, Computer And Communications Security, 2006). Other successful attacks against biometric encryption include hill climbing attacks (“Vulnerabilities in Biometric Encryption Systems”, by Andy Adler in IAPR Audio and Video-Based Biometric Person Authentication, 2005). These attacks render these two classes of systems unacceptably insecure. A final problem with these approaches is that, like 966, '375, '554, '239 and '261 discussed above, they are non-invertible and can only be generated from the raw biometric—meaning that if compromised or if the user wants to change the key, they must physically reenroll. This means that they cannot be varied on a per-transaction basis. If captured via Phising or a compromise of the data store, they can be used to attack the original system. Furthermore, a system operator is less likely to inform users and incur the costs of reenrollment of all users, unless there is definitive evidence of a security breach.
In summary, the prior art provides a base for biometric-based security technologies and key management but is lacking in the important respects of protecting the biometric data and the embedded keys. It is also lacking in operational situations because of the need to have users reenroll to issue new biometric-based identity tokens or embed new keys.